GregCocks, to Futurology
@GregCocks@techhub.social avatar
GregCocks, to France
@GregCocks@techhub.social avatar

Spatiotemporal Analysis Is 17,000 Years Old – Or Maybe More?

https://subscription.packtpub.com/book/programming/9781837639175/2/ch02lvl1sec05/history-of-geospatial-analysis <-- shared text

https://doi.org/10.1017/S0959774322000415 <-- shared paper

[Snow and Picquet were not first, as amazing as they were...]
“Geospatial analysis can be traced back as far as 17,000 years ago [or more], to the Lascaux cave in southwestern France. In this cave, Paleolithic artists painted commonly hunted animals and what many experts recently concluded are dots representing the animals’ lunar cycles to note seasonal behavior patterns of prey, such as mating or migration…”
#GIS #spatial #mapping #spatialanalysis #spatiotemporal #LascauxCave #France #archaeology #artwork #Paleolithic #artists #humanoid #lunarcycles #seasonal #model #modeling #abstract #relationships #knowledgegraphs

photos - Examples of animal depictions associated with sequences of dots/lines. (a) Aurochs: Lascaux, late period; (b) Aurochs: La Pasiega, late; (c) Horse: Chauvet, late...; (d) Horse: Mayenne-Sciences, early; (e) Red Deer: Lascaux, late; (f) Salmon: Abri du Poisson, early; (g) Salmon (?): Pindal, late; (h) Mammoth: Pindal, early.

GregCocks, to lunar
@GregCocks@techhub.social avatar
GregCocks, to uk
@GregCocks@techhub.social avatar
GregCocks, to worldwithoutus
@GregCocks@techhub.social avatar

How Melting Arctic Ice Leads To European Drought And Heatwaves

https://insideclimatenews.org/news/01032024/links-between-melting-arctic-ice-and-summertime-extreme-weather-in-europe/ <-- shared technical article

https://doi.org/10.5194/wcd-5-109-2024 <-- shared paper

#GIS #spatial #mapping #spatialanalysis #spatiotemporal #arctic #climate #climatechange #europe #ice #meltingice #drought #heatwaves #Greenland #northatlantic #model #modeling #numericmodeling #interaction #inflow #freshwater #weather #research #water #hydrology #marine #oceangraphy #climatology #extremeweather

maps - The Combined Drought Indicator—used to identify areas affected by agricultural drought, and areas with the potential to be affected—estimated for the first 10 days of each month from April to September 2022. Credit: European Commission, Joint Research Centre
photo - The Wamme river is seen at a low level during the European heatwave on Aug 10, 2022 in Rochefort, Belgium
maps - Climatological mean (a) SST, (d) meridional winds at 700 hPa, (g) 2 m air temperature, and (j) precipitation minus evaporation in summer (May through to August). Regressions of (b, c) the SST (colour shading) and 700 hPa winds (arrows), (e, f) the meridional winds at 700 hPa, (h, i) the 2 m air temperature, and (k, l) the accumulated precipitation minus evaporation on FE in (b, e, g, k) the first and (c, f, h, l) the second summer (May through to August) after the freshwater anomalies (indicated by the “+1” and “+2” in the titles). We removed large-scale trends from the air temperature to reduce the direct warming effect of greenhouse gases (Sect. 2), and we excluded the anomaly in 2016 since it exhibited a different spatial SST distribution from the other anomalies (Fig. A1). Thick contours encompass regions that are significant at the 95 % confidence level, and the red and blue dotted lines in (b) and (c) delineate the regions in which the SST anomalies exceed 2 ∘C and fall below −2 ∘C.

GregCocks, to 3dmodeling
@GregCocks@techhub.social avatar

Future Changes In Global Atmospheric Rivers Projected By CMIP6 Models

https://doi.org/10.1029/2023JD039359 <-- shared paper

#GIS #spatial #mapping #atmosphericriver #risk #hazard #3dmodeling #spatialanalysis #spatiotemporal #climate #climatechange #extreme #model #modeling #numericmodeling #precipitation #rain #rainfall #global #atmosphere #CMIP6 #climatology #temperature #surfacetemperature #heavyrain #hydrology #water #hydrological #hydrologicalcycle #globalwarming #thermaleffects #naturalhazard #naturaldisaster #disaster

maps and charts - (a) Atmospheric river (AR) characteristics, including duration, interval, area, and intensity during the DJF globally. The central plot displays the increase in AR frequency between the far-future (SSP585; 2070–2099) and the historical (1980–2009) periods, with the climatological AR frequency in the historical period represented by the contours. Black dots indicate unanimous frequency changes among CMIP6 models. The red rectangles are the boundaries of the target regions and the black hatches are the selected land regions. The surrounding plots show 2D Kernel Density Estimation (KDE) maps for AR characteristics over the historical period and far-future under different scenarios displayed in the bottom right corner for eight regions. The univariate distributions of the characteristics are shown on the outer axes of each subplot. (b) The comparison of the KDE maps for three selected regions with ARs detected with GuanWaliser_v2 and Mundhenk_v3 AR detection tools (ARDTs). Note that only the SSP585 scenario is available for the far-future period in these two data sets. The legend is the same as in Panel (a). Note that the figures of PanLu ARDT are presented based on daily scale data sets, while the figures of GuanWaliser_v2 and Mundhenk_v3 ARDTs are presented on 6-hourly scale data sets due to data accessibility.
graphic - what is the science behind atmospheric rivers
maps and charts - Projected increase in panel (a) atmospheric river (AR) frequency and (b) AR-induced precipitation for DJF and JJA in Northern Hemisphere and Southern Hemisphere under the SSP585 scenarios. The shading represents the differences between the far-future (2070–2099) and historical (1980–2009) periods, with contours delineating the historical climatology. ARs detected with the PanLu, GuanWaliser_v2 and Mundhenk_v3 method are all shown. Note that the figures of PanLu ARDT are presented based on daily scale data sets, while the figures of GuanWaliser_v2 and Mundhenk_v3 ARDTs are presented on 6-hourly scale data sets due to data accessibility.

GregCocks, to RadioControl
@GregCocks@techhub.social avatar

The Case For Remote Sensing Of Individual Plants

https://doi.org/10.1002/ajb2.1347 <-- shared short article

# interpretation

aerial images - High-resolution images from the Planet Labs constellation of cube-sats detect flowering individual trees in the Peruvian Amazon (yellow objects in panel A) and Colombian Amazon (pink objects in panel B). Many thousands of flowering individuals are apparent across hundreds of kilometers of Amazonian forest in these flowering events. Scale bar = 500 m.
aerial and oblique remotesensing-created images - Drone remote sensing of individual trees. (A) Ultra-high-density drone lidar resolves individual tree structure in a temperate beech forest in the southern Czech Republic. Colors indicate elevation, and the tallest trees are about 40 m aboveground. Measurement density here is 4323 points per square meter. (B) High-spatial resolution optical remote sensing from a low-altitude drone in the Atlantic lowlands of Costa Rica. We used methods from computer vision to construct three-dimensional scene geometry from two-dimensional images. The image is a natural color composite. (C) Same area as B, but colored by surface elevation, where warmer colors indicate taller objects. A single Goethalsia meiantha crown is outlined in white. The area of this crown is 157.3 m2. At a pixel size of 1 cm, this crown contains 1.573 × 106 pixels, demonstrating the tremendous increase in measurement density at high-spatial resolution. Scale bar in B and C = 30 m.
graphic / schematic - drone performing remote sensing on a tree

GregCocks, to geopolitics
@GregCocks@techhub.social avatar
GregCocks, to climate
@GregCocks@techhub.social avatar
GregCocks, to climate
@GregCocks@techhub.social avatar
underdarkGIS, to opensource
@underdarkGIS@fosstodon.org avatar

Congratulations to the MobilityDB team on the joint release candidate 1.1 of , , and 🎉

➡️ https://github.com/MobilityDB/MobilityDB/releases/tag/v1.1.0rc1
➡️ https://github.com/MobilityDB/PyMEOS/releases/tag/pymeos-1.1.3rc1

The most important change in v1.1 was to extract the core functionality for temporal and from MobilityDB into the Engine (MEOS) C library. https://libmeos.org/

In this way, the same functionality provided by MobilityDB in a database environment is available in a C (...)

GregCocks, to geopolitics
@GregCocks@techhub.social avatar
GregCocks, to geopolitics
@GregCocks@techhub.social avatar
GregCocks, to australia
@GregCocks@techhub.social avatar
GregCocks, to conservative
@GregCocks@techhub.social avatar

Shipwreck Ecology - Understanding The Function And Processes From Microbes To Megafauna

https://doi.org/10.1093/biosci/biad084 <-- shared paper

[as a scuba diver who has done some wreck diving in various places around the world, there is an extra layer of interest here for me…]

photos - examples - shipwreck habitats
maps - shipwrecks - globally
graphic - fundamental ecological functions and processes occurring on shipwrecks

GregCocks, to geopolitics
@GregCocks@techhub.social avatar
underdarkGIS, to opensource
@underdarkGIS@fosstodon.org avatar

is an data management and analytics platform for (https://github.com/MobilityDB/MobilityDB). Its core function is to efficiently store and query tracks, such as vehicle . It implements the specification. MobilityDB is engineered from and , providing via

More in Esteban's talk on
https://www.crunchydata.com/community/events/postgis-day-2023

GregCocks, (edited ) to climate
@GregCocks@techhub.social avatar
GregCocks, to ArtificialIntelligence
@GregCocks@techhub.social avatar

Environmental Scanning Of Cocaine Trafficking In Brazil - Evidence From Geospatial Intelligence And Natural Language Processing Methods

https://doi.org/10.1016/j.scijus.2023.09.002 <-- shared paper

“• Geospatial intelligence contributes to the formulation of public drug policies
• Concentrated individual cocaine seizures linked to specific geographical features
• News websites provide valuable insights into drug trafficking dynamics
• The identified routes and trends align with current literature on drug trafficking
• São Paulo - key node for Brazil's cocaine traffic and its international distribution”

image/jpeg
image/jpeg
image/jpeg

GregCocks, to climate
@GregCocks@techhub.social avatar
GregCocks, to geopolitics
@GregCocks@techhub.social avatar
GregCocks, to Futurology
@GregCocks@techhub.social avatar
underdarkGIS, to random
@underdarkGIS@fosstodon.org avatar

Every once in a while, I stumble over a paper that has really nice figures, that make me curious to learn how they were created

Fiona Lippert et al.'s "Learning to predict dynamics from weather radar networks" is definitely one of them

Luckily they provide their plotting code at https://github.com/FionaLippert/FluxRGNN/blob/v.1.1.1/notebooks/radar_case_study.ipynb for all of us to learn from

GregCocks, to geopolitics
@GregCocks@techhub.social avatar
GregCocks, to climate
@GregCocks@techhub.social avatar
  • All
  • Subscribed
  • Moderated
  • Favorites
  • megavids
  • kavyap
  • DreamBathrooms
  • GTA5RPClips
  • magazineikmin
  • InstantRegret
  • cubers
  • thenastyranch
  • Youngstown
  • rosin
  • slotface
  • osvaldo12
  • ngwrru68w68
  • ethstaker
  • JUstTest
  • everett
  • Durango
  • normalnudes
  • Leos
  • mdbf
  • khanakhh
  • tester
  • modclub
  • cisconetworking
  • anitta
  • tacticalgear
  • provamag3
  • lostlight
  • All magazines